ALBERT

Description: This paper presents a palaeoenvironmental reconstruction of the Wilczków fen (central Poland). The fen developed in an inactive valley at the onset of the Holocene (~11 ka BP) and peat accumulation lasted until 5.7 ka BP. Multi-proxy reconstructions were made on the basis of palaeobotanical, cladoceran, chironomid, beetle and geochemical analyses. A Kohonen self-organizing map (SOM, unsupervised artificial neural network) of the biotic sequence distinguished four stages of fen history. Stage X 1 (11.0–10.7 ka BP) was relatively wet and cool. Organic matter started to accumulate but the habitat conditions remained unstable. Moss, sedge and fern communities then developed. Sedimentary changes reveal an intensive groundwater supply at that time. Numerous and diverse chironomid and cladoceran subfossils indicate nearly permanent aquatic conditions. During stage Y 1 (10.6–9.2 ka BP) conditions were dry and the upper peat layer desiccated. Cladocera nearly disappeared whereas chironomids were represented by semi-terrestrial and predatory (Tanypodinae) species. Conditions started to be more reducing. All the remaining samples belonged to the interweaving stages X 2 and Y 2 . Stage Y 2 (mostly 9.1–7.3 and 6.0–5.7 ka BP) was also dry but humidity increased towards the top. Oxidizing conditions occurred and the pH became more alkaline, favouring Cladium mariscus . The basin received mostly allochthonous matter input at that time. Stage X 2 (mostly 6.8–6.1 ka BP) was humid and warm. The groundwater supply remained low but there was an increase in precipitation, changing local conditions to ombrotrophic. Species-rich chironomid and cladoceran communities were associated with temporary pools. Finally, conditions returned to those characteristic of stage Y 2 . The presented reconstruction documents long-term abiotic and biotic changes determined by water supply, including groundwater outflow, which have rarely been detected at a multi-proxy scale. We show that inactivated valley fens are sensitive to climate-driven hydrological fluctuations. Kohonen neural networks appear to be a promising method for analysing variability in multi-proxy data.